Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues.
Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website.
This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.
Table of Contents
Introduction and Examples
Methods for Ignorable Missing Data
Missing Data Mechanisms
Linear and Generalized Linear Mixed Models
Generalized Estimating Equations
Time-to-event data analysis
Survival function and hazard function
Estimation of a survival function
Cox's semiparametric multiplicative hazards models
Accelerated failure time models with time-independent covariates
Accelerated failure time model with time-dependent covariates
Methods for competing risks data
Overview of Joint Models for Longitudinal and Time-to-Event Data
Joint Models of Longitudinal Data and an Event time
Joint Models with Discrete Event Times and Monotone Missingness
Longitudinal Data with Both Monotone and Intermittent Missing Values
Event Time Models with Intermittently Measured Time Dependent Covariates
Longitudinal Data with Informative Observation Times
Dynamic Prediction in Joint Models
Joint Models for Longitudinal Data and Continuous Event Times from Competing Risks
Joint Alaysis of Longitudinal Data and Competing Risks
A Robust Model with t-Distributed Random Errors
Ordinal Longitudinal Outcomes with Missing Data Due to Multiple Failure Types
Bayesian Joint Models with Heterogeneous Random Effects
Accelerated Failure Time Models for Competing Risks
Joint Models for Multivariate Longitudinal and Survival Data
Joint Models for Multivariate Longitudinal Outcomes and an Event Time
Joint Models for Recurrent Events and Longitudinal Data
Joint Models for Multivariate Survival and Longitudinal Data
Joint Models and Missing Data: Assumptions, Sensitivity Analysis, and Diagnostics
Variable Selection in Joint Models
Joint Multistate Models
Joint Models for Cure Rate Survival Data
Sample Size and Power Estimation for Joint Models
A Software to Implement Joint Models
Robert Elashoff, Gang Li, Ning Li
"This book is a comprehensive state-of-the-art treatment of joint models for time-to-event and longitudinal data with numerous applications to real-world problems. … [T]his book is a comprehensive review of the existing literature on joint models, including most extensions of these models, fully parametric or not, for multiple events and multiple markers with a special focus on missingness problems and details about various estimation methods. By emphasizing the most advanced methods, this book usefully completes existing monographs on joint models and will be a helpful reference book for researchers in biostatistics and experienced statisticians, while applied statisticians could also be interested thanks to the numerous examples of real data analyses."
—Helene Jacqmin-Gadda, University of Bordeaux, in Biometrics, March 2018
"This book provides an extensive survey of research performed on the subject of joint models in longitudinal and time-to-event data. … The authors’ expertise in this area shines through their careful attention to detail in presenting the wide variety of settings in which these models can be applied. Overall, I consider the book to be a valuable and rich resource for introducing and promoting this relatively new area of research. … Where this book primarily succeeds is in the great care taken by the authors in walking through the necessary details of these joint models and the breadth of topics they cover. When topics are left out, the authors refer to a large body of literature to which the interested reader can look to further their understanding. …
I would recommend it either as a handy reference for researchers or as a graduate level reference text in a specialized course … [I]t is truly rich with useful content that can be extracted and applied with due diligence. …. I certainly consider it a valuable addition to my bookshelf for personal reference and, should the need arise, I would be happy to refer it to others who might encounter such data in their work.
—Caleb B. King, Sandia National Laboratories, in the Journal of the American Statistical Association, October 2017
"The book starts with a clear description of the work (i.e. topics developed and fields of application) as well as some examples that illustrate the use of joint models.) … The book finishes with a chapter devoted to the review of model assumption assessment, variable selection, multistate models, cure rate survival data and sample size estimation. Approaches for sensitivity analysis are shown. A general index of sensitivity to non-ignorability is also presented. A brief but good motivation for the use of a joint modelling approach of variable selection is given. A full section of the chapter regards Bayesian approaches. Joint multistate models and cure rate survival are briefly mentioned. … An extensive and useful bibliography is given at the end of the book."
— Silvana Romio, ISCB News, May 2017
"This book provides the most comprehensive and in-depth coverage of the topic of joint modeling of longitudinal and survival data....A unique feature of this book in comparison to other related books or review papers is its broad yet in-depth coverage of the topics in joint modeling, which include: (i) monotone or intermittent non-ignorable missing data triggered by a single event, such as death, or multiple types of events, (ii) intermittently measured time-dependent covariates, which may be further subject to measurement errors, and (iii) longitudinal data with informative observational times. Extension to multivariate longitudinal and/or survival data as well as event-times subject to competing risks are also covered. In addition to theory and methodology, applications are also emphasized in this book, with a collection of available software listed in the Appendix. This is a useful book for anyone wishing to dive into the joint modeling paradigm as well as a good resource for seasoned researchers. It is well suited for a graduate course."
—Jane-Ling Wang, Distinguished Professor of Statistics, University of California, Davis, June 2016
"Many good books are now available that treat the analysis of repeated measurement and time-to-event data as separate topics. However, in many longitudinal studies both types of data are collected and a joint approach is required in order to exploit fully the information in the data. This book is a very welcome addition to the literature on joint modelling. It contains a nice blend of theory and applications, and would be an excellent text for a graduate course in biostatistics or as a manual for practising biostatisticians."
—Peter J Diggle, CHICAS, Lancaster University Medical School, May 2016
"…A clearly well-written book covering a broad range of topics on joint modelling of longitudinal data and time-to-event data that will, without doubt, serve as a valuable reference for researchers interested in this field. At the same time, this timely and comprehensive overview is accessible to those with almost no background in this area and practitioners with a large collection of applications, real data and online data sources. This book could also serve as a valuable textbook for graduate students in statistics or biostatistics due to its balance of methodology and practical examples."
—Jianguo Sun, University of Missouri, May 2016